Integration of multiple signaling pathway activities
resolves K-RAS/N-RAS mutation paradox in colon
epithelial cell response to inflammatory cytokine
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Citation
Kreeger, Pamela K. et al. “Integration of multiple signaling
pathway activities resolves K-RAS/N-RAS mutation paradox in
colon epithelial cell response to inflammatory cytokine
stimulation.” Integrative Biology 2.4 (2010): 202.
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http://dx.doi.org/10.1039/B925935J
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Royal Society of Chemistry
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www.rsc.org/ibiology | Integrative Biology
Integration of multiple signaling pathway activities resolves
K-RAS/N-RAS mutation paradox in colon epithelial cell response
to inflammatory cytokine stimulationw
Pamela K. Kreeger,z*a Yufang Wang,bc Kevin M. Haigisbc and
Douglas A. Lauffenburger*ad
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Received 9th December 2009, Accepted 1st February 2010
First published as an Advance Article on the web 8th March 2010
DOI: 10.1039/b925935j
Colon tumors frequently harbor mutation in K-RAS and/or N-RAS, members of a GTPase
family operating as a central hub for multiple key signaling pathways. While these proteins are
strongly homologous, they exhibit diverse downstream effects on cell behavior. Utilizing an
isogenic panel of human colon carcinoma cells bearing oncogenic mutations in K-RAS and/or
N-RAS, we observed that K-RAS and double mutants similarly yield elevated apoptosis in
response to treatment with TNFa compared to N-RAS mutants. Regardless, and in surprising
contrast, key phospho-protein signals were more similar between N-RAS and dual mutants.
This apparent contradiction could not be explained by any of the key signals individually, but a
multi-pathway model constructed from the single-mutant cell line data was able to predict the
behavior of the dual-mutant cell line. This success arises from a quantitative integration of
multiple pro-apoptotic (pIkBa, pERK2) and pro-survival (pJNK, pHSP27) signals in manner
not easily discerned from intuitive inspection.
Introduction
Upon activation by receptor tyrosine kinases, the RAS family
of GTPases (K-RAS4A, K-RAS4B, H-RAS, and N-RAS)
signal to multiple downstream effector pathways. Single amino
acid mutations at codons 12, 13, or 61 place RAS in a
chronically active (GTP-bound) state and are oncogenic.1
While K-RAS and N-RAS are greater than 90% homologous
a
Department of Biological Engineering, Massachusetts Institute of
Technology, 77 Massachusetts Ave, 16-343, Cambridge, MA 02139,
USA. Tel: +1 (617) 252-1629
b
Molecular Pathology Unit and Center for Cancer Research,
Massachusetts General Hospital, Charlestown, MA 02129, USA
c
Department of Pathology, Harvard Medical School, Boston,
MA 02115, USA
d
Center for Cancer Research, Massachusetts Institute of Technology,
Cambridge, MA 02139, USA. E-mail: lauffen@mit.edu
w Financial support: This work was funded by National Institutes of
Health Grants U54-CA112967 and P50-GM68762, and by the
American Cancer Society (PF-08-026-01-CCG to P.K.K).
z Current contact: University of Wisconsin-Madison, Department of
Biomedical Engineering.
and share many of the same downstream effectors,2 several
lines of evidence indicate that the RAS isoforms have distinct
physiological functions. For example, mouse models of
K-RasG12D and N-RasG12D expressed in the colonic epithelium
show distinct phenotypes, with K-RasG12D stimulating
hyper-proliferation and N-RasG12D conferring resistance to
apoptosis.3 Oncogenic K-RAS promotes butyrate-induced
apoptosis in human colon carcinoma cells4 while N-RAS
provides anti-apoptotic signals in mouse embryonic
fibroblasts,5 indicating that apoptosis is a key cellular process
that is differentially regulated by the RAS family members.
Historical data for colon cancer suggest that mutations in
K-RAS and N-RAS can co-exist in an individual tumor,6
raising the question of why a given tumor might select for two
mutations that exert opposite effects on a key oncogenic
process such as apoptosis. To help address this question, we
examine here the impact of simultaneous mutation of both
N-RAS and K-RAS on the response of colon carcinoma cells
to exposure to the inflammatory cytokine tumor necrosis
factor-a (TNFa), which is appreciated to be intimately
Insight, innovation, integration
Activating mutations in the RAS GTPases have been
identified in over 30% of all cancers; however, the impact
of these mutations on the cellular signaling network and
phenotypic behavior is generally confusing. Here, we utilize a
novel systems biology approach that integrates experimental
and computational techniques to analyze the impact of
activating mutations in K-RAS, N-RAS, either alone or in
202 | Integr. Biol., 2010, 2, 202–208
combination, in human colon epithelial cells on the response
to the inflammatory cytokine TNFa. Through our analysis,
we have identified an important quantitative balance
between multiple pro-survival versus pro-apoptotic pathways that resolves seemingly contradictory behavior of dual
RAS mutant cells relative to cells with either individual
mutation.
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involved in tumor progression in the colon as well as in other
tissues.7 We observe that the dual-mutant cells phenocopy
K-RAS single mutant cells with respect to their apoptotic
response to TNFa even while signaling patterns for key
pathways in the dual mutants are more similar to those in
N-RAS mutant cells. To resolve this paradox, we construct a
multi-pathway partial least squares regression (PLSR) model
from the single mutant cell data and test its ability to predict
the sensitivity of the dual-mutant cells. We find that this
model successfully predicts the extent of apoptosis for the
dual-mutant condition, on the basis of a quantitative balance
between multiple pro-apoptotic signals (pERK2, pIkBa) and
pro-survival signals (pJNK, pHSP27). The capability of this
single model to comprehend cellular information processing
with respect to cytokine-challenged survival, invariantly across
the different RAS mutation genotypes, implies that the cells
could transition relatively seamlessly from one mutation to
another (or have both co-exist) during dynamic changes in
degree of inflammatory context that might in some cases be
problematic for the K-RAS mutation.8
Materials and methods
Cell lines and treatments
DLD-1, a colon carcinoma cell line with a single copy
K-RASG13D mutation, and DKs8-N, which over-express
mutant N-RASG12V in an isogenic wild-type K-RAS
background, have previously been described.9,10 DLD-N were
generated by infecting DLD-1 cells with pBabe retrovirus
carrying N-RasG12D. All cell lines were maintained in DMEM
supplemented with 10% fetal bovine serum (FBS). For
experiments, cells were plated in 10% FBS at 15 000 cells cm2;
after 24 h, cells were sensitized with 200 units mL1 interferon-g
(IFNg, Roche Applied Science, Indianapolis, IN) in 5% FBS.
After 24 h, cells were treated with either vehicle or 100 ng mL1
TNFa (Peprotech, Rocky Hill, NJ). Data are represented as
average SEM for three biological replicates.
RAS characterization
The levels of active K- and N-RAS were assessed with a RAS
activation assay kit (Upstate, Billerica, MA). Briefly, cells were
washed twice with ice-cold PBS and then lysed with MLB
buffer (25 mM HEPES, pH 7.5, 150 mM NaCl, 1% Igepal
CA-630, 10 mM MgCl2, 1 mM EDTA and 2% glycerol)
containing protease and phosphatase inhibitors (Roche
Applied Science). After centrifugation at 14 000 g for 5 min,
protein levels were quantified using the Bio-Rad Protein Assay
Kit (Bio-Rad Laboratories, Hercules, CA). Cell lysates
containing 1 mg of protein were incubated at 4 1C for 120 min
with 10 mL of agarose bound with glutathione S-transferase
fusion protein corresponding to the human RAS-binding
domain (RBD, residues 1–149) of RAF1. After the samples
were washed three times with MLB, active RAS was eluted
and the levels of active K- and N-RAS were determined by
Western blotting analysis. Membranes were blocked with
Odyssey Blocking Buffer (Li-Cor, Lincoln, NE), probed
with anti-N-RAS (sc-31) or anti-K-RAS (sc-30, Santa Cruz,
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Biotechnology, Santa Cruz, CA), detected with Donkeyanti-mouse-800, and imaged on the Odyssey infrared imaging
system (Li-Cor). 30 mg of cell lysate was also analyzed for
total levels of K- and N-RAS with the same antibodies and
anti-tubulin (Sigma, St. Louis, MO).
Lysis and signaling measurements
At various times after TNFa stimulation (0, 5, 15, 30, 60, 90,
120, 240, 480, and 720 min) cells were lysed using Bio-Plex
cell lysis buffer (Bio-Rad). Total protein concentrations
were determined using the bicinchoninic acid assay (Pierce,
Rockford, IL). The levels of four phospho-proteins (pERK2,
pIkBa, pJNK, and pHSP27) were detected using commercially
available kits for the Luminex system (Bio-Rad). These
proteins were chosen as they are in the ‘middle layer’ of several
key signal transduction pathways and were informative in our
prior studies.11 pHSP27 was chosen as a readout of p38, which
did not provide a robust signal by this method in our
preliminary studies (data not shown). A master positivereference sample was loaded in each assay for normalization
purposes. Full time courses were performed once for
each condition with three biological replicates and technical
duplicates. Median CV’s were 10.8% (technical duplicates)
and 18.7% (biological replicates). A subset of timepoints were
independently repeated to determine overall variation,12 with
a media CV of 16.9%, indicating that assay, biological, and
day-to-day variation are of similar magnitude and the data set
is self-consistent. Importantly, in the repeated samples,
trends within and between samples were preserved. Data is
represented as average SEM. Hierarchical clustering analysis
on the resulting data set was performed in Spotfire (TIBCO,
Palo Alto, CA) with the unweighted pair group method
using arithmetic averages (UPGMA) and Euclidean distances.
The integrated level of each signal was determined by
trapezoid rule.13
Flow cytometry
Floating and adherent cells were pooled and analyzed for
apoptosis using cleaved caspase-3/cleaved PARP similar to the
previously described methods.12 Cells were fixed with 4%
paraformaldehyde, permeabilized with Tween, and stained
using anti-cleaved caspase-3 (1 : 500) and anti-cleaved PARP
(1 : 250, BD Pharmingen, Franklin Lakes, NJ), followed by
Alexa 488-donkey-anti-rabbit IgG and Alexa 647-donkeyanti-mouse IgG (both at 1 : 250, Invitrogen, Carlsbad, CA).
A minimum of 25 000 cells per condition was analyzed on a
BD Biosciences LSRII (part of the Koch Institute Flow
Cytometry Core Facility, MIT) and by FlowJo (Tree Star,
Inc, Ashland, OR).
Partial least squares regression (PLSR) modeling
Multi-pathway models were generated using the PLSR
algorithm in SimcaP (Umetrics, Kinnelon, NJ—see ref. 14
for details). Briefly, an independent block matrix (X,
dimensions MxN) was generated with each column corresponding
to the data for a single time point and phosphoprotein.
There were 40 columns in total (10 times 4 phosphoproteins).
Each row represented a different cellular condition, with a
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total of four rows corresponding to untreated and TNFa
treated DLD-1 and DKs8-N cells. A dependent matrix
(Y, dimensions MxP) was generated from the cellular
output data, with the rows corresponding to the same cellular
conditions listed above and three columns for the level of
apoptosis at each time. All data were mean-centered and unit
variance scaled. PLSR was used to solve the regression
problem:
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Y = Xb + e
(1)
where b is the vector containing the regression coefficients and
e is the residual. A nonlinear iterative partial least squares
(NIPALS) algorithm was used.15 It is instructive to note that
the NIPALS algorithm applied to a single MxN matrix (X) is
the representation of the matrix as a sum of outer products
such that:
X = t1p1 0 + t2p2 0 + + e
(2)
where ti is called the scores vector and represents one dimension
in the orthogonal basis set for the column space and pi is called
the loadings vector and represents one dimension in the
orthogonal basis set for the row space. The NIPALS
algorithm was implemented as described elsewhere.16 Briefly,
an iterative process is used to define two vectors, w and c, that
maximize the following term:
[Cov(t,u)]2 = [Cov(Xw,Yc)]2
(3)
where t and u are the scores vectors for the X and Y matrices,
respectively. Loadings vectors for X and Y, called p and q,
respectively, are also defined as:
p = XTt/(tTt); q = YTu/(uTu)
(4)
The set of vectors t,u,w, and c are associated with the
maximum eigenvalues for various covariance matrices, and
once defined, their contribution is removed from the X and Y
matrices, leaving a residual matrix that can be further modeled
with a new set of t,u,w,c,p, and q vectors. Each model was
tested for goodness of prediction (Q2) using a leave-one-out
cross validation approach.16 Briefly, cross-validation was
performed by omitting an observation from the model
development and then using the model to predict the Y-matrix
values for the withheld observation. This procedure was
repeated until every observation has been kept out exactly
once. To predict the novel DLD-N condition and analyze the
impact of individual phosphoprotein timecourses, the resulting
Y matrix for each new X signaling matrix was determined by
the derived PLSR function.
Results
To explore the impact of single versus dual mutations in RAS,
we utilized a panel of isogenic colon carcinoma cell lines with
either a mutant form of K-RAS (DLD-1), mutant N-RAS
(DKs8-N), or both mutations (DLD-N, depicted in Fig. 1A).
Western blot analysis confirmed the elevation in active K-RAS
in DLD-1 and DLD-N and active N-RAS in DLD-N and
DKs8-N (Fig. 1B). As expected, there is an increase in total
N-RAS levels with the over-expression of active N-RAS.
Interestingly, N-RAS over-expression also results in a slight
204 | Integr. Biol., 2010, 2, 202–208
Fig. 1 Cells with mutations in both N-RAS and K-RAS are sensitive
to TNFa. (A) Overview of the RAS genotypes of the three cell lines.
(B) Levels of active (RBD) and total K- and N-RAS for each cell line.
(C) The percentage of apoptotic cells was determined by flow
cytometry from the percentage of cells that were positive for both
cleaved caspase-3 and cleaved PARP. (D) DLD-1 and DLD-N cells
demonstrated a similar sensitivity to TNFa, while DKs8-N cells were
more resistant.
increase in total K-RAS, preserving the ratio of N-RAS to
K-RAS. TNFa treatment of this panel of cell lines results in a
significant increase in apoptosis as measured by positive
staining for cleaved caspase-3 and cleaved PARP (Fig. 1C).
We have previously demonstrated that DLD-1 cells were more
sensitive to TNFa treatment than DKs8-N;11 here, we
observed that DLD-N cells were as sensitive to TNFa as
DLD-1 cells (Fig. 1D). Forty-eight hours after TNFa
treatment, 27% of DLD-N cells and 29% of DLD-1 cells
stained positive for cleaved caspase-3/cleaved PARP, while
only 14% of DKs8-N cells were apoptotic. Thus, the effect of
having both K-RAS and N-RAS mutated on TNFa-induced
cell death is to yield essentially the same apoptosis-sensitization
as the K-RAS mutation by itself. In essence, mutant K-RAS is
epistatic to mutant N-RAS with respect to TNFa-induced
apoptosis.
Previously, we examined the dynamics of several
phosphorylated proteins across key signaling pathways
downstream of TNFa treatment in DLD-1 and DKs8-N cells,
and found that these signals were sufficient to predict
apoptosis and chemokine levels.11 Accordingly, we quantified
the levels of phosphorylated ERK2, HSP27, IkBa, and JNK in
DLD-N cells following TNFa treatment using high-throughput,
multiplexed assays and the Luminex platform (Fig. 2A).
TNFa treatment led to increased levels of each of the
phosphoproteins examined, with dynamics that differed based
on the genetic configuration of the cell. For example, DLD-1
cells demonstrated weak activation of pERK2, pJNK,
and pHSP27, but robust increases in pIkBa. In contrast,
DKs8-N and DLD-N cells showed activation of all proteins.
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Fig. 2 Signaling dynamics in response to TNFa treatment.
(A) Individual time courses for the four phosphoproteins indicated
that DKs8-N and DLD-N cells had similar signaling dynamics.
(B) Cluster analysis by UPGMA confirmed the similarity between
DKs8-N and DLD-N cells. Each box represents the scaled average
signal for a particular time, ordered from early to late times.
The dynamics of pERK2 were strikingly different between
DLD-N and DKs8-N or DLD-1 cells, with elevated levels of
pERK2 seen for longer time periods. Overall, the pattern of
signaling behavior between DKs8-N and DLD-N was similar,
and analysis by hierarchical clustering confirmed that DLD-N
and DKs8-N clustered separately from DLD-1 within both the
untreated and TNFa-treated branches (Fig. 2B). In surprising
contrast to the apoptosis results, then, the effect of having
both K-RAS and N-RAS mutated on TNFa-induced cell
signaling is fairly similar to that of the N-RAS mutation by
itself.
Accordingly, we face a paradox: the behavior of double
K-/N-RAS mutation in response to TNFa is similar to N-RAS
mutation by itself with respect to signaling but is similar to
K-RAS mutation by itself with respect to apoptosis. In order
to understand how this puzzle might be resolved, we use a
systems modeling approach, partial least-square regression
(PLSR), previously found to be helpful in ascertaining
the most informative combinations of signals for predicting
phenotypic behavior.11 In PLSR, the X matrix (phosphoprotein
levels) is regressed against the Y matrix (apoptosis),
and dimensionality is reduced by combining independent
measurements that strongly co-vary with the dependent
outcomes into principal components.15 The first principal
component captures the strongest variation in the original
data matrix, while succeeding principal components capture
remaining variation. In our previous studies of various
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individual mutations, the N-RAS mutation effects and
K-RAS mutation effects were clearly distinguishable by their
quantitative differences in contribution to the second principle
component, while the first principle component mainly
characterized the effects of TNFa treatment.
Employing this methodology here, a PLSR model
constructed from the signaling and response data for
untreated and TNFa treated DLD-1 and Dks8-N cells was
found to fit the data well, with an R2Y of 0.95 and a Q2Y of
0.75 (Fig. 3A). Previous reports by our group have utilized the
integrated signal intensity or time derivatives as metrics within
the X matrix of the model.17,18 However, since these metrics
are a linear combination of signals, it adds no new information
in PLSR, which is a linear model form; rather, it could be used
to represent the contribution of individual timepoints in
aggregate. Given the small size of the X matrix in this new
study, we found that the individual data points provide more
substantial contributions. Similar to our previous model
incorporating wildtype-RAS cells,11 the projection of the
various conditions along the scores of the model demonstrated
that the first component was a ‘TNFa treatment’ axis
(Fig. 3B). Confirming this interpretation, signaling data from
pERK2 and pIkBa weighed strongly in the first component
(Fig. 3C); these signals result from TNFa treatment by virtue
of TNFR activation of the NFkB pathway and transactivation
of a TGFa autocrine loop.11,13,19 pHSP27 and pJNK have
negative loadings in the second component, in agreement with
their activation in DKs8-N cells that have negative scores in
the second component.
PLSR models might be generally expected to predict similar
phenotypic responses for conditions with similar signaling
patterns. In the double RAS mutants, however, we identified
similar signaling behavior, but divergent apoptotic response,
between DLD-N and DKs8-N cells. Therefore, we might have
expected that the PLSR model constructed from single RAS
mutant cells would have difficulty predicting the response of
double mutant cells. Nonetheless, in a direct test of the
model’s ability to predict DLD-N apoptosis from the
DLD-N signaling data we found successful prediction of
elevated sensitivity of the DLD-N double mutants compared
to DKs8-N (Fig. 3A).
To understand conceptually how the PLSR model was able
to reconcile the apparent contradiction between DLD-N
signaling behavior and apoptotic response, we generated a
series of modified DLD-N data sets, with three of four
phospho-protein time-courses from DLD-N experimental
data and the fourth a substitution of either DLD-1 or
DKs8-N data. For example, for the pERK2 substitution,
pJNK, pHSP27, and pIkBa data would all be used from the
DLD-N cells, while pERK2 time courses would be substituted
from either the DLD-1 or DKs8-N cells. Using these mixed
data sets, apoptosis was predicted from the PLSR model
(Fig. 4A); in this way, we could modify the characteristics of
each set of phospho-proteins to parse its contribution. For
example, DLD-N has elevated pERK2 relative to both cell
lines (Fig. 2A) and pERK2 contributes positively to apoptosis
(component 1, Fig. 3C). Correspondingly, substitution of
either DLD-1 or DKs8-N pERK2 signaling into the DLD-N
data set resulted in a prediction of apoptosis that was lower
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Fig. 3 A PLSR model built on single-mutant signaling and apoptosis is able to predict dual-mutant apoptosis. (A) The PLSR model accurately
predicted the elevated sensitivity of DLD-N cells to TNFa. (B) Scores for the three cell lines in the first and second component. The first
component is consistent with a ‘TNFa treatment’ axis. (C) Loadings for the first two components of the single-mutant PLSR model. CP indicates
the loadings for the response metric of cleaved caspase-3 and cleaved PARP positive cells.
than the intact DLD-N data set (Fig. 4A). Similarly, DLD-N
cells have lower pIkBa than DLD-1 cells; when a prediction
was made with DLD-1 levels of pIkBa, apoptosis increased as
would be expected from the heavy positive loading of pIkBa
for apoptosis. Substitution of the elevated pERK2 or lower
pIkBa levels from DLD-N to the DLD-1 data set also
increased the predicted apoptosis (data not shown). In the
second component of the model (Fig. 3C), pJNK contributed
negatively to apoptosis; as DLD-N have elevated pJNK
relative to both of the single mutant cell lines (Fig. 2A), the
decrease in pJNK with the single mutant substitution resulted
in a small increase in apoptosis (Fig. 4A). pHSP27 does not
contribute as strongly or consistently in the model loadings
and substitution of pHSP27 had only a slight impact on the
predicted level of apoptosis. To quantitatively determine the
impact of each signaling change, we calculated the integrated
level for each signal’s timecourse and determined the difference
between the DLD-N integrated level and the integrated level
from the substitute data (Table 1). Comparing the differences
between DLD-N and mixed data set apoptosis predictions,
Table 1 Quantification of the difference in integrated signal level and
the total difference in predicted apoptosis between DLD-N and the
substituted data set
Signal
Substitution
Change in
integrated signal
Change in total
predicted apoptosis
pERK2
DKs8-N
DLD-1
DKs8-N
DLD-1
DKs8-N
DLD-1
DKs8-N
DLD-1
420
520
22
210
7.9
210
120
340
40
47
3.2
3.8
3.1
14
11
23
pHSP27
pIkBa
pJNK
and the change in each integrated signal, we were able to
identify the model interpretation for each signal (Fig. 4B).
pIkBa and pERK2 are both pro-apoptotic while pJNK and
pHSP27 are pro-survival influences. We conclude that the
PLSR model predominantly monitors the combined contributions
from pERK2 and pIkBa with counteracting combined
contributions from pJNK and pHSP27. Hence, in DLD-N
Fig. 4 (A) In silico predictions using mixed data sets demonstrate the balance between different signaling components. Apoptosis was predicted
from the PLSR model using the full DLD-N signaling data set (black) versus data sets with three DLD-N time-courses and a single protein
time-course substituted from DKs8-N (DN, red) or DLD-1 (DLD, blue). (B) Summary of the observed impact of changing signal data on the
prediction of apoptosis.
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dual-mutant cells heightened sensitivity to TNFa arises from
increased pERK2 more than from enhanced pIkBa, inverting
the relative contributions of these two pro-apoptotic signals
compared to those in DLD-1 K-RAS mutant cells.
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Discussion
Utilizing a multi-pathway model, we were able to predict the
apoptotic response of a novel cell line expressing mutant forms
of both K-RAS and N-RAS, which normally exert differential
effects on the cellular response to TNFa. Following treatment
with TNFa, DLD-N cells demonstrated activation of pERK2,
pJNK, pHSP27, and pIkBa similar to N-RAS single mutant
cells, while apoptosis was similar to K-RAS single mutant
cells. Examination of the model demonstrated that the
successful prediction resulted from the model interpretation
of information from multiple pathways. Elevated pERK2 or
pIkBa was interpreted by the model to correspond to
increased apoptosis. After TNFa treatment, DLD-N exhibited
the highest levels of pERK2 (which the model interpreted as
more apoptosis) and pIkBa levels below DLD-1 (moderating
the prediction of apoptosis). An analogous balance mechanism
has been reported between signals involved in endocytosis and
PI3K signaling in HER2 over-expressing mammary epithelial
cells.14 It is important to remember that PLSR models are
correlative, and therefore, signals such as pIkBa and pERK2
that correlate to TNFa treatment may not be causative of
apoptosis. In our previous studies of the single-mutant cell
lines, experimental evidence suggested that pERK signaling
that resulted from TNFa transactivation of TGFa had a net
pro-death effect.11
Our motivation for this present work was to gain insights
concerning how two mutations in the RAS pathway that exert
opposite effects on apoptosis integrate to yield a cell fate
decision. One potential effect would be for these mutations
to exert additive or synergistic effects on another oncogenic
pathway, for example proliferation or differentiation.
However, our results indicate that with respect to
TNFa-induced apoptosis, dual mutant cells phenocopy
K-RAS mutant cells rather than showing a moderated level
of apoptosis. Moreover, in vivo evidence does not support this
interpretation for K-RAS and N-RAS. When expressed in the
mouse colonic epithelium, mutationally activated K-Ras
promotes proliferation, suppresses differentiation, and confers
sensitivity to apoptotic stimuli.3 Mutant N-Ras, by contrast,
has no effect on proliferation or differentiation, but actively
suppresses apoptosis.3 One possible explanation for the
co-existence of apparently contradictory mutations is that
the mutations arise during circumstances of transient selective
pressure. Currently available cancer genome sequencing
data suggests that KRAS mutation is more effective at
promoting sporadic colon cancer progression than is mutant
NRAS—KRAS mutations occur in 30–40% of colon cancers,
but NRAS mutations occur in only 3–5%. The rarity of
N-RAS mutations suggests that they might arise under special
selective pressure. Given the effect of activated N-RAS on the
cellular response to TNFa, a pro-inflammatory cytokine,
we suspect that NRAS mutations occur in cancers exposed
to inflammation. Because of its deleterious effect with respect
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to apoptosis, KRAS mutation would likely not be advantageous
during inflammation, and, indeed, KRAS mutations are less
frequent in human colon cancers associated with chronic
inflammation.20 If the inflammation were transient, however,
NRAS mutations might arise early during tumor progression,
but then be expendable later in tumor progression because of
regression of the inflammation. The loss of inflammation
would set-up a permissive state for KRAS mutation.
In essence, mutant N-RAS would allow the cancer to adapt
to a transient selective pressure, but would later become a
passenger mutation.
Conclusions
Integrative systems models such as the multi-pathway
model of apoptosis presented here may provide a platform
to identify key control mechanisms regulating cell behavior,
resulting in novel drug targets. Our findings emphasize that
signal-to-response relationships require multiple signaling
pathways to be included in the model, as it is the balance of
these signals that is interpreted by cells to make decisions.
Acknowledgements
We wish to acknowledge Roli Mandhana for her assistance in
collecting lysate samples and members of the Lauffenburger
lab for helpful discussions. We gratefully acknowledge Glenn
Paradis of the Koch Institute Flow Cytometry Core Facility
for technical assistance. This work was funded by National
Institutes of Health Grants U54-CA112967 and P50-GM68762,
and by the American Cancer Society (PF-08-026-01-CCG to
P.K.K).
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